Announcement for Downloading full text filePlease respect the Copyright Act.
All digital full text dissertation and theses from this website are authorized the copyright owners. These copyrighted full-text dissertation and theses can be only used for academic, research and non-commercial purposes. Users of this website can search, read, and print for personal usage. In respect of the Copyright Act of the Republic of China, please do not reproduce, distribute, change, or edit the content of these dissertations and theses without any permission. Please do not create any work based upon a pre-existing work by reproduction, Adaptation, Distribution or other means.
URN etd-0722115-161922 Statistics This thesis had been viewed 754 times. Download 103 times. Author Chun-Che Chen Author's Email Address No Public. Department Computer Science and Enginerring Year 2014 Semester 2 Degree Ph.D. Type of Document Doctoral Dissertation Language English Page Count 93 Title An Efficient Image Retrieval Scheme Using Binarized SIFT Features and Look-up Tables Keyword Hashing Image retrieval SIFT feature Feature binarization Feature binarization SIFT feature Image retrieval Hashing Abstract In image retrieval, the well-known SIFT is capable of extracting distinctive features and has been widely used in many fields. However, it is time consuming in matching the features, which slows down the entire process and becomes its major drawback. In the SIFT matching, the Euclidean distance is used as the measurement between two vectors. The calculation of the distance is expensive because it involves the calculation of square of numbers. On the other hand, the scale of the image database usually is too large to adopt linear search for image retrieval. To improve the SIFT matching, this dissertation proposes a fast image retrieval scheme that transforms the SIFT features to binary representation. Accordingly, the complexity of the matching process can be reduced to a much simpler bit-wise operation, which greatly decreases the retrieval time. Furthermore, the proposed scheme utilizes look-up tables (LUT) with four layers of indexes to retrieve similar images. The indexes are derived from the binarized features and can further speed up the retrieval process. Experiments were conducted to examine the usefulness of the binary representation and the LUT, and to demonstrate the effectiveness and efficiency of the proposed scheme. SIFT method and two other methods were also tested for comparison. The experimental results show that the proposed scheme can retrieve images efficiently with comparable accuracy to SIFT and outperforms the other two methods. Advisor Committee Shang-Lin Hsieh - advisor
Chiung-San Lee - co-chair
Shuenn-Shyang Wang - co-chair
Tsang-Long Pao - co-chair
Yo-Ping Huang - co-chair
Yue-Shan Chang - co-chair
Files Date of Defense 2015-05-12 Date of Submission 2015-07-23